The design scheme of an agricultural expert system based on longan and cauliflower planting techniques is presented. Using an object-oriented design and a combination of the techniques in multimedia, database, expert ...The design scheme of an agricultural expert system based on longan and cauliflower planting techniques is presented. Using an object-oriented design and a combination of the techniques in multimedia, database, expert system and artificial intelligence, an in-depth analysis and summary are made of the knowledge features of die agricultural multimedia expert system and data models involved. According to the practical problems in agricultural field, the architectures and functions of the system are designed, and some design ideas about the hybrid knowledge representation and fuzzy reasoning are proposed.展开更多
A method of knowledge representation and learning based on fuzzy Petri nets was designed. In this way the parameters of weights, threshold value and certainty factor in knowledge model can be adjusted dynamically. The...A method of knowledge representation and learning based on fuzzy Petri nets was designed. In this way the parameters of weights, threshold value and certainty factor in knowledge model can be adjusted dynamically. The advantages of knowledge representation based on production rules and neural networks were integrated into this method. Just as production knowledge representation, this method has clear structure and specific parameters meaning. In addition, it has learning and parallel reasoning ability as neural networks knowledge representation does. The result of simulation shows that the learning algorithm can converge, and the parameters of weights, threshold value and certainty factor can reach the ideal level after training.展开更多
Knowledge representation is a key to the building of expert systems. The performance of knowledge representation methods directly affects the intelligence level and the problem-solving ability of the system. There are...Knowledge representation is a key to the building of expert systems. The performance of knowledge representation methods directly affects the intelligence level and the problem-solving ability of the system. There are various kinds of knowledge representation methods in ESEP3.0. In this paper, the authors introduce the knowledge representation methods, such as structure knowledge, seismological and precursory forecast knowledge, machine learning knowledge, synthetic prediction knowledge, knowledge to validate and verify certainty factors of anomalous evidence and support knowledge, etc. and propose a model for validation of certainty factors of anomalous evidence. The knowledge representation methods represent all kinds of earthquake prediction knowledge well.展开更多
It is becoming an important social problem to make maintenance and rehabilitation of existing infrastructures such as bridges, buildings, etc. in the world. The kernel of such structure management is to develop a meth...It is becoming an important social problem to make maintenance and rehabilitation of existing infrastructures such as bridges, buildings, etc. in the world. The kernel of such structure management is to develop a method of safety assessment on items<span style="font-family:;" "=""> </span><span style="font-family:;" "="">which include remaining life and load carrying capacity. The purpose of this paper is to summarize the finding of up-to-date research articles concerning the application of knowledge-based systems to assessment and management of structures and to illustrate the potential of such systems in the structural engineering. In here, knowledge-based systems include knowledge-based expert systems incorporation with artificial neural networks, fuzzy reasoning and genetic or immune algorithms.</span><span style="font-family:;" "=""> </span><span style="font-family:;" "="">Specifically, two modern bridge management systems (BMS’s) are presented in the paper. The first is a BMS to assess the performance and derive optimal strategies for inspection and maintenance of concrete bridge structures using reliability based and knowledge-based systems. The second is the concrete bridge rating expert system (<i>J-BMS BREX</i>) to evaluate the performance of existing bridges by incorporating with artificial neural networks and fuzzy reasoning.</span>展开更多
语义Web模糊知识的表示和应用常常涉及模糊隶属度比较,但现有描述逻辑的模糊扩展缺乏描述模糊隶属度比较的能力.提出支持模糊隶属度比较和描述逻辑ALCN(attributive concept description language with complements and number restrict...语义Web模糊知识的表示和应用常常涉及模糊隶属度比较,但现有描述逻辑的模糊扩展缺乏描述模糊隶属度比较的能力.提出支持模糊隶属度比较和描述逻辑ALCN(attributive concept description language with complements and number restriction)概念构造子的扩展模糊描述逻辑FCALCN(fuzzy comparable ALCN).FCALCN引入新的原子概念形式以支持模糊隶属度比较.给出FCALCN的推理算法,证明了在空TBox约束下FCALCN的推理问题复杂性是多项式空间完全的.FCALCN能够表达语义Web上涉及模糊隶属度比较的复杂模糊知识并实现对它们的推理.展开更多
针对模糊Petri网存在隶属度单一的问题,将直觉模糊集理论与Petri网理论相结合,构建直觉模糊Petri网(Intuitionistic Fuzzy Petri Nets,IFPN)模型,用于知识的表示和推理.首先构建了IFPN模型,并将其应用于知识的表示,通过在模型中引入抑...针对模糊Petri网存在隶属度单一的问题,将直觉模糊集理论与Petri网理论相结合,构建直觉模糊Petri网(Intuitionistic Fuzzy Petri Nets,IFPN)模型,用于知识的表示和推理.首先构建了IFPN模型,并将其应用于知识的表示,通过在模型中引入抑止转移弧,解决了否命题的表示问题.其次提出了基于矩阵运算的IFPN推理算法,通过修改变迁触发后token值的传递规则,解决了推理过程中的事实的保留问题;通过修改变迁的触发规则,抑制了变迁的重复触发.最后对推理算法进行了分析,并举例验证了提出的IFPN模型及其推理算法的可行性,结果表明IFPN是对FPN的有效扩充和发展,其对推理结果的描述更加细腻、全面.展开更多
扩展模糊描述逻辑是对描述逻辑的一种模糊扩展,支持对复杂模糊知识的表示和推理,但该逻辑缺乏支持术语公理约束的推理算法.提出扩展模糊描述逻辑EFALCR+(extended fuzzy attributive concept description language with complements and...扩展模糊描述逻辑是对描述逻辑的一种模糊扩展,支持对复杂模糊知识的表示和推理,但该逻辑缺乏支持术语公理约束的推理算法.提出扩展模糊描述逻辑EFALCR+(extended fuzzy attributive concept description language with complements and transitive roles)的受限TBox(terminological box)描述术语公理,给出受限TBox约束下的EFALCR+推理算法,并对该算法进行优化,证明优化后的算法是正确完备的,时间复杂性不超过指数,最后证明受限TBox约束下的EFALCR+推理问题是指数时间完全问题.优化算法的最坏时间复杂性已达到该问题推理算法的复杂度下界,是实现术语公理约束下模糊知识库推理的有效算法.展开更多
基金Supported by the National Natural Science Foundation of China (No. 700400D1).
文摘The design scheme of an agricultural expert system based on longan and cauliflower planting techniques is presented. Using an object-oriented design and a combination of the techniques in multimedia, database, expert system and artificial intelligence, an in-depth analysis and summary are made of the knowledge features of die agricultural multimedia expert system and data models involved. According to the practical problems in agricultural field, the architectures and functions of the system are designed, and some design ideas about the hybrid knowledge representation and fuzzy reasoning are proposed.
文摘A method of knowledge representation and learning based on fuzzy Petri nets was designed. In this way the parameters of weights, threshold value and certainty factor in knowledge model can be adjusted dynamically. The advantages of knowledge representation based on production rules and neural networks were integrated into this method. Just as production knowledge representation, this method has clear structure and specific parameters meaning. In addition, it has learning and parallel reasoning ability as neural networks knowledge representation does. The result of simulation shows that the learning algorithm can converge, and the parameters of weights, threshold value and certainty factor can reach the ideal level after training.
文摘Knowledge representation is a key to the building of expert systems. The performance of knowledge representation methods directly affects the intelligence level and the problem-solving ability of the system. There are various kinds of knowledge representation methods in ESEP3.0. In this paper, the authors introduce the knowledge representation methods, such as structure knowledge, seismological and precursory forecast knowledge, machine learning knowledge, synthetic prediction knowledge, knowledge to validate and verify certainty factors of anomalous evidence and support knowledge, etc. and propose a model for validation of certainty factors of anomalous evidence. The knowledge representation methods represent all kinds of earthquake prediction knowledge well.
文摘It is becoming an important social problem to make maintenance and rehabilitation of existing infrastructures such as bridges, buildings, etc. in the world. The kernel of such structure management is to develop a method of safety assessment on items<span style="font-family:;" "=""> </span><span style="font-family:;" "="">which include remaining life and load carrying capacity. The purpose of this paper is to summarize the finding of up-to-date research articles concerning the application of knowledge-based systems to assessment and management of structures and to illustrate the potential of such systems in the structural engineering. In here, knowledge-based systems include knowledge-based expert systems incorporation with artificial neural networks, fuzzy reasoning and genetic or immune algorithms.</span><span style="font-family:;" "=""> </span><span style="font-family:;" "="">Specifically, two modern bridge management systems (BMS’s) are presented in the paper. The first is a BMS to assess the performance and derive optimal strategies for inspection and maintenance of concrete bridge structures using reliability based and knowledge-based systems. The second is the concrete bridge rating expert system (<i>J-BMS BREX</i>) to evaluate the performance of existing bridges by incorporating with artificial neural networks and fuzzy reasoning.</span>
文摘语义Web模糊知识的表示和应用常常涉及模糊隶属度比较,但现有描述逻辑的模糊扩展缺乏描述模糊隶属度比较的能力.提出支持模糊隶属度比较和描述逻辑ALCN(attributive concept description language with complements and number restriction)概念构造子的扩展模糊描述逻辑FCALCN(fuzzy comparable ALCN).FCALCN引入新的原子概念形式以支持模糊隶属度比较.给出FCALCN的推理算法,证明了在空TBox约束下FCALCN的推理问题复杂性是多项式空间完全的.FCALCN能够表达语义Web上涉及模糊隶属度比较的复杂模糊知识并实现对它们的推理.
文摘针对模糊Petri网存在隶属度单一的问题,将直觉模糊集理论与Petri网理论相结合,构建直觉模糊Petri网(Intuitionistic Fuzzy Petri Nets,IFPN)模型,用于知识的表示和推理.首先构建了IFPN模型,并将其应用于知识的表示,通过在模型中引入抑止转移弧,解决了否命题的表示问题.其次提出了基于矩阵运算的IFPN推理算法,通过修改变迁触发后token值的传递规则,解决了推理过程中的事实的保留问题;通过修改变迁的触发规则,抑制了变迁的重复触发.最后对推理算法进行了分析,并举例验证了提出的IFPN模型及其推理算法的可行性,结果表明IFPN是对FPN的有效扩充和发展,其对推理结果的描述更加细腻、全面.
文摘扩展模糊描述逻辑是对描述逻辑的一种模糊扩展,支持对复杂模糊知识的表示和推理,但该逻辑缺乏支持术语公理约束的推理算法.提出扩展模糊描述逻辑EFALCR+(extended fuzzy attributive concept description language with complements and transitive roles)的受限TBox(terminological box)描述术语公理,给出受限TBox约束下的EFALCR+推理算法,并对该算法进行优化,证明优化后的算法是正确完备的,时间复杂性不超过指数,最后证明受限TBox约束下的EFALCR+推理问题是指数时间完全问题.优化算法的最坏时间复杂性已达到该问题推理算法的复杂度下界,是实现术语公理约束下模糊知识库推理的有效算法.
